Probability and Statistics for Computer Science
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Beschreibung
This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning.
With careful treatment of topics that fill the curricular needs for the course, Probability and Statistics for Computer Science features:
• A treatment of random variables and expectations dealing primarily with the discrete case.
• Achapter dealing with classification, explaining why it’s useful; how to train SVM classifiers with stochastic gradient descent; and how to use implementations of more advanced methods such as random forests and nearest neighbors.
• A chapter dealing with regression, explaining how to set up, use and understand linear regression and nearest neighbors regression in practical problems.• A chapter dealing with principal components analysis, developing intuition carefully, and including numerous practical examples. There is a brief description of multivariate scaling via principal coordinate analysis.
• A chapter dealing with clustering via agglomerative methods and k-means, showing how to build vector quantized features for complex signals.
Illustrated throughout, each main chapter includes many worked examples and other pedagogical elements such as
boxed Procedures, Definitions, Useful Facts, and Remember This (short tips). Problems and Programming Exercises are at the end of each chapter, with a summary of what the reader should know. Instructor resources include a full set of model solutions for all problems, and an Instructor's Manual with accompanying presentation slides.list of terms a student should understand, list of facts a student should keep in mind, list of procedures a student should be able to use, list of practical skills a student should have absorbed. All end-of-chapter elements reference to their discussions within the chapter A clear but crisp account of probability, structured specifically to the needs of the undergraduate computer science student Many exercises and examples using a wide range of real published datasets throughout, focusing on content that is likely to be used in practice Easy-to-understand but careful treatment of topics, with much emphasis on exploratory data analysis and descriptive statistics Topics of great practical importance (like classification, clustering, regression, and principal components analysis) covered at an undergraduate level, with an emphasis on using methods in practice on real datasets Text broken up throughout with handy and useful sidebars which help explain the content in real time, including worked examples so that the reader can self-assess while absorbing the material Each chapter ends with: list of definitions a student sh Includes supplementary material: sn.pub/extras Request lecturer material: sn.pub/lecturer-material
Autor*in
David Forsyth
Themen in »Probability and Statistics for Computer Science«
Summarizing 1D data Boxplots Datasets Spatial data Random variables Conditional probability Expected values Discrete distributions Continuous distributions Confidence intervals Clustering Markov chains regression Significance of evidence